David Sloan Wilson begins his essay with a claim that is both bracing and, to my mind, correct: our multiple crises are not simply failures of intention; they are failures of conception. We act in terms of what we can see—and what we can see is guided by our paradigms. If our paradigms reliably “make sense” while reliably producing ruin, then “better people” won’t save us; better ways of seeing and doing might.
I’m with David all the way in his insistence that paradigms must be evaluated not only by fidelity to facts, but by whether they inform appropriate action. That second criterion is the one that matters most to the future of the world. But it also raises the hardest question: appropriate action in the service of what?
Paradigms Don’t Act—People Do, and They Do So Toward Aims
From the standpoint of philosophy of science as I practice it (functional contextualism), truth is not a mirror held up to nature. Truth is what works—what yields prediction-and-influence with precision, scope, and depth—in the service of stated purposes. The “purpose” clause is not an ornament. It is the hinge.
That’s why I’m uneasy whenever debates about “primary units” become metaphysical. David is right to reject the atomistic individualism that has dominated orthodox economics and much of the social sciences. But the cure is not to enthrone “the group” as the new primary unit—at least not if we are serious about moving from explanation to intentional change.
In evolutionary thinking, there are no “primary units” floating free of aims. There are only functional units—units that help us analytically (our goal) because they help us see how life evolves with regard to a goal, a context, and a history. When our aim is to help a particular cooperative system become more adaptive as a system (what David elsewhere distinguishes from mere aggregates of individually strategizing agents), we must ask an explicitly pragmatic question: At what level, in this context, toward this goal, are the strongest and most modifiable selection pressures operating right now—and what intervention would shift them safely?
Sometimes that is indeed “group-level governance.” Sometimes it is “individual-level repertoires.” Often it is both—because human cognition, human values, and human identity are social through and through. The applied issue is not individualism vs. the group. The applied issue is what level(s) must be targeted, in what sequence, for this system with this history, to move toward a valued future.
The Real Paradigm Shift: From Describing Evolution to Evolving On Purpose
David ends with a deceptively simple line: every prosocial change effort should be treated as an experiment in cultural evolution. I want to underline that—but also sharpen it.
That sentence gestures toward the deeper paradigm shift: evolving on purpose.
Evolutionary scientists have often been socialized to treat “purpose” and “consciousness” as contaminants—words that invite teleology, moralizing, and (after the horrors of eugenics) justified ethical alarm. Yet the irony is that the evolution of consciousness, properly understood, is precisely what makes deliberate evolution possible in the first place. Consciousness, at its bone level, is action “with knowledge”—the capacity to respond to self and world and their regularities. Once that capacity evolves—especially in symbolic form—there is a kind of biphasic break (Hayes & Hofmann, 20xx) because then variation can be generated, selected, and retained not only by blind environmental pressures, but by if/then/better formulations: plans, values, rules, narratives, models.
When we use those symbolic capacities to create sustained changes in cultural practices—and those changes persist because they work—we are watching “conscious evolution” unfold in real time. Not cosmic teleology. Not wishful thinking. Just the recognition that deliberate, values-linked variation/selection/retention is itself an evolved capability.
If this is right, then the “new paradigm” is not merely “complexity + evolution.” It is complexity + evolution + intentionality—and intentionality here means values, ethics, and the disciplined scientific humility to treat our interventions as provisional, testable moves inside non-linear systems.
The Ergodicity Trap: Why “Mathematical Equivalence” Can Mislead Action
Those interested in multi-level selection – David included -- sometimes say kin selection and multilevel selection are “mathematically equivalent.” In a narrow, formal sense—given a particular partitioning of covariance terms and a particular set of assumptions—one can indeed map explanatory pieces across frameworks. Fine.
But deliberate action based on this knowledge is where such equivalence breaks.
When you move from explaining how cooperation can evolve in principle to changing how cooperation can be made to evolve in practice, you face a basic fact about complex adaptive biopsychosocial systems: they are non-ergodic. That is, the average over an ensemble (a population statistic) is not the same thing as the average over time for a particular lineage, person, or group with a particular history. In non-ergodic systems, path dependence isn’t an “add-on complication.” It is the point.
All forms of population genetics lean on aggregate statistics—relatedness terms, average costs and benefits, population-level covariances. That is exactly where the hidden ergodic assumption creeps in: we quietly behave as though ensemble summaries are reliable guides to time-bound trajectories in context. You can patch “path dependence” back in after the fact (and many do), but the patch admits the deeper issue: ensemble averages blur the very steps that intentional change must take.
Multilevel selection has an enormous advantage here—not because “the group” is primary, but because MLS naturally invites you to ask: what selection pressures are operating within multiple levels of analysis over time on context (from the gut biome to cultural processes) and what would shift that balance to serve the interests of particular people (individual, couple, families, organizations, neighborhoods, nations, the world)? The phrase “particular people” is consciously chosen because it can fit all of these levels – it does not simply mean “an individual” defined by the skin.
MLS falls into a linguistic trap if it is practiced as a discourse about generic “groups” rather than as a method for changing selection pressures for particular people here, now, toward a clear goal.
So here is the crux: the important divide is not traditional evolutionary accounts vs MLS. The important divide is ergodic, average-based inference vs. idionomic, trajectory-based intervention. For this view of paradigms to matter it’s an issue we must confront.
Idionomics: The Kind of Information Intentional Change Requires
If we take seriously David’s call to treat prosocial change efforts as experiments, then we must also take seriously what kind of data experimentation needs. The most important information for evolving on purpose is not merely what tends to work for an abstraction (averages impact on an ensemble. It is idionomic—information that respects particularity while remaining scientific (Hayes, Ciarrochi, Hofmann, Chin, & Sahdra, 2022).
By idionomics I mean:
- the rigorous study of change dynamics within particular units of importance over time,
- with measurement dense enough to capture that change process, not just outcome,
- and with models and decisions tethered to a local aim (the valued direction that defines success).
In clinical intervention science, we learned a painful lesson: if you only measure outcomes at long intervals and only analyze ensemble averages, you can easily miss what actually produces change for particular people. The same is true—magnified—in cultural and organizational change. The actionable causal story lives in the evolving pattern of context, behavior, and consequence, nested inside symbolic meaning systems and social contingencies.
That is why the natural home for prosocial work is not just dissemination. It is what we once described it as a field station for cultural evolutionary studies—a place where basic processes and applied change are forced to meet, repeatedly, in the messy reality of real people at multiple level of analysis.
The Crucible: Experimental Analysis as a Moral and Scientific Necessity
I want to end with a plea that is simultaneously scientific and moral. If we are going to “evolve on purpose,” we need more than inspiring frameworks. We need the crucible of experimental analysis: rapid-cycle testing; repeated measurement; transparency; replication; and a willingness to change the program itself based on what is learned.
Why? Because conscious evolution is powerful—and power without disciplined feedback is precisely how well-intentioned people produce ruin. We do not get to hide behind good intentions. We do not get to hide behind elegant theory. We do not even get to hide behind Nobel-prize-winning observations.
If Prosocial is to be a true exemplar of the complexity/evolution paradigm, it must embody the very method David recommends: learning by doing—but doing so in a way that produces cumulative, public, and correction-friendly knowledge. That means investing in measurement infrastructure and process research.
I’m optimistic for the same reason David is optimistic: the paradigm we need is already emerging. But my version of the punchline is ever so slightly different. The new paradigm is not simply seeing the world as complex, holistic, and evolved. The new paradigm is based on the experimental analysis needed to learn—safely, transparently, and ethically—how to evolve on purpose.
References
Hayes, S. C., Ciarrochi, J., Hofmann, S. G., Chin, F., & Sahdra, B. (2022). Evolving an idionomic approach to processes of change: Towards a unified personalized science of human improvement. Behaviour Research and Therapy, 156, 104155. DOI: 10.1016/j.brat.2022.104155
Hayes, S. C. & Hofmann, S. G. (2023). A biphasic relational approach to the evolution of human consciousness (Un enfoque relacional bifásico para la evolución de la conciencia humana). International Journal of Clinical and Health Psychology, 24(4), 100380. DOI: 10.1016/j.ijchp.2023.100380
